Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B
Name
P19-1303.pdf
Description
Published version
Size
508.56 KB
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Unknown
Checksum (MD5)
5a9b0364ee35f8e763995696b8232eab
Author(s)
Luo, Jiaming
Cao, Yuan
Barzilay, Regina
Date Issued
August 2019
Journal
ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Publisher
Association for Computational Linguistics (ACL)
Citation
Luo, Jiaming, Cao, Yuan and Barzilay, Regina. 2019. "Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B." ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference.
Version
Final published version
Abstract
© 2019 Association for Computational Linguistics In this paper we propose a novel neural approach for automatic decipherment of lost languages. To compensate for the lack of strong supervision signal, our model design is informed by patterns in language change documented in historical linguistics. The model utilizes an expressive sequence-to-sequence model to capture character-level correspondences between cognates. To effectively train the model in an unsupervised manner, we innovate the training procedure by formalizing it as a minimum-cost flow problem. When applied to the decipherment of Ugaritic, we achieve a 5.5% absolute improvement over state-of-the-art results. We also report the first automatic results in deciphering Linear B, a syllabic language related to ancient Greek, where our model correctly translates 67.3% of cognates.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.18653/V1/P19-1303
